The accumulation of omics data at multiple levels provides an opportunity to better understand the progression of chronic liver disease (CLD) to hepatocellular carcinoma (HCC). A variety of HCC- associated molecular alterations have been detected. However, due to the lack of good diagnostic markers and treatment strategies, and because of the disease heterogeneity in human populations, a coherent understanding of the mechanism of HCC development is still limited. The assessment of complex multigenic molecular pathways in HCC remains a difficult challenge. This project brings together experts in bioinformatics, biostatistics, biochemistry, clinical cancer research, and mass spectrometry to EM Algorithm, Posterior Mode</keyword;/keywords;dat treatment of HCC. Specifically, this project Evaluate metabolic changes in the progression of CLD to HCC in serum and plasma samples by an ultra-performance liquid chromatography coupled with a quadrupole time of flight mass spectrometry (UPLC-QTOF MS). Serum and plasma samples collected from newly diagnosed HCC cases and matched cirrhotic controls will be utilized. The identified metabolic biomarkers will be verified by comparing their tandem mass spectrometry data with those generated from commercially available standard compounds. (2) Investigate key metabolic and signaling pathways that may be altered in the progression of CLD to HCC. Specifically, we will utilize a pathway-centric approach by integrating experimental findings from multiple studies, including our previous proteomics and glycomics studies, to provide a "molecular map" of changes in HCC to aid in the design of targets for diagnostic and therapeutic development. We anticipate the outcome of this study to enhance our understanding of the disease progression and the functional involvement of candidate HCC biomarkers in metabolic and signaling pathways. PUBLIC HEALTH RELEVANCE: Defining clinically applicable biomarkers that detect early-stage hepatocellular carcinoma (HCC) in a high-risk population of cirrhotic patients has potentially far-reaching consequences for disease management and patient health. This project is important because most HCC patients are diagnosed at a late stage, where the treatment options are limited. There is a pressing need to identify biomarkers that could be used for early detection of HCC. This project will capitalize on markers identified in this and other studies to investigate fingerprints that may be related to the progression of HCC. In addition to screening high-risk populations for early signs of disease, the identified biomarkers and knowledge of their functional involvement in metabolic and signaling pathways could be used to design and test improved treatment strategies.